R Graphics Gallery

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An R graphics gallery has been created with simple scripts for long-term reference and to expedite data visualization.

Simple Line Plot

Click to enlarge

x <- 1:10
y <- c(4, 3, 2, 5, 6, 7, 1, 8, 9, 7)
plot(x, y, 
     type = "b",
     main = "Simple Plot",
     xlab = "X-axis Title",
     ylab = "Y-axis Title",
     col = "red3")

x <- 1:10

y <- c(4, 3, 2, 5, 6, 7, 1, 8, 9, 7)

plot(x, y,

type = "b",

main = "Simple Plot",

xlab = "X-axis Title",

ylab = "Y-axis Title",

col = "red3")

Bar Plot of Vector

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# Bar plot of vector
set.seed(1)
y.vec <- sample(-10:10,10, rep = TRUE)
barplot(y.vec,
        col = "steelblue4",
        names = LETTERS[1:10],
         ylim=c(-10,10))
abline(h = 0)

# Bar plot of vector

set.seed(1)

y.vec <- sample(-10:10,10, rep = TRUE)

barplot(y.vec,

col = "steelblue4",

names = LETTERS[1:10],

ylim=c(-10,10))

abline(h = 0)

Bar Plot of Matrix: #1

Click to enlarge

# Bar plot of matrix: #1
set.seed(10)
y.mat <- matrix(sample(1:10, 60, rep = TRUE), nrow = 6)
barplot(y.mat, 
        col = colorRampPalette(c("steelblue4", "green"))(6),
        names=LETTERS[1:10],
        ylim=c(0, 50))
abline(h = 0)

# Bar plot of matrix: #1

set.seed(10)

y.mat <- matrix(sample(1:10, 60, rep = TRUE), nrow = 6)

barplot(y.mat,

col = colorRampPalette(c("steelblue4", "green"))(6),

names=LETTERS[1:10],

ylim=c(0, 50))

abline(h = 0)

Bar Plot of Matrix: #2

Click to enlarge

# Bar plot of matrix: #2
barplot(y.mat,
        col=colorRampPalette(c("steelblue4", "green"))(6),
        names=LETTERS[1:10],
        horiz = TRUE,
        legend = paste("Sensor", 1:6),
        xlim=c(0,50))
abline(v = 0)

# Bar plot of matrix: #2

barplot(y.mat,

col=colorRampPalette(c("steelblue4", "green"))(6),

names=LETTERS[1:10],

horiz = TRUE,

legend = paste("Sensor", 1:6),

xlim=c(0,50))

abline(v = 0)

Box Plot

Click to enlarge

# Box plot
y <- split(rnorm(1000), rpois(1000,2.2))
boxplot(y,
        col="orange",
        main="Boxplot",
        xlab="Poisson Distribution",
        ylab="Normal Distribution",
        ylim=c(-3, 3))

# Box plot

y <- split(rnorm(1000), rpois(1000,2.2))

boxplot(y,

col="orange",

main="Boxplot",

xlab="Poisson Distribution",

ylab="Normal Distribution",

ylim=c(-3, 3))

In order to access the values behind the chart, simply set the boxplot() argument plot=FALSE. A statistical summary is generated.

Contour Plot

Click to enlarge

# Contour plot
# Generate data
rx <- range(x <- 10 * 1:nrow(volcano))
ry <- range(y <- 10 * 1:ncol(volcano))
ry <- ry + c(-1, 1) * (diff(rx) - diff(ry))/2

# Define graph parameters
tcol <- colorRampPalette(c("forestgreen", "maroon", "red"))(12)
opar <- par(mfrow = c(2, 2), mar = rep(0, 4))
par(opar)
opar <- par(pty = "s", bg = "white")
u <- par("usr")

# Open and configure blank device
plot(x = 0,
     y = 0,
     type = "n",
     xlim = rx,
     ylim = ry,
     xlab = "",
     ylab = "")

# Add rectangle layer
u <- par("usr")
rect(u[1], u[3], u[2], u[4], col = "ivory")

# Add contours
contour(x,
        y,
        volcano,
        col = tcol,
        lty = "solid",
        add = TRUE,
        vfont = c("sans serif", "plain"))

# Add plot elements
title("Topography of Maunga Whau Volcano", font = 4)
abline(h = 200*0:4, v = 200*0:4, col = "black", lty = 2, lwd = 0.2)
abline(h = 341, v = 300, col = "blue", lwd = .4)

# Contour plot

# Generate data

rx <- range(x <- 10 * 1:nrow(volcano))

ry <- range(y <- 10 * 1:ncol(volcano))

ry <- ry + c(-1, 1) * (diff(rx) - diff(ry))/2

# Define graph parameters

tcol <- colorRampPalette(c("forestgreen", "maroon", "red"))(12)

opar <- par(mfrow = c(2, 2), mar = rep(0, 4))

par(opar)

opar <- par(pty = "s", bg = "white")

u <- par("usr")

# Open and configure blank device

plot(x = 0,

y = 0,

type = "n",

xlim = rx,

ylim = ry,

xlab = "",

ylab = "")

# Add rectangle layer

u <- par("usr")

rect(u[1], u[3], u[2], u[4], col = "ivory")

# Add contours

contour(x,

volcano,

col = tcol,

lty = "solid",

add = TRUE,

vfont = c("sans serif", "plain"))

# Add plot elements

title("Topography of Maunga Whau Volcano", font = 4)

abline(h = 200*0:4, v = 200*0:4, col = "black", lty = 2, lwd = 0.2)

abline(h = 341, v = 300, col = "blue", lwd = .4)

Dot Chart

Click to enlarge

# Dot chart
# Configure data
x <- mtcars[order(mtcars$mpg), ]
x$cyl <- factor(x$cyl)
x$color[x$cyl == 4] <- "violetred"
x$color[x$cyl == 6] <- "green3"
x$color[x$cyl == 8] <- "royalblue"

dotchart(x$mpg,
         labels = row.names(x),
         cex = 0.85,
         pch = 16,
         groups = x$cyl,
         main = "Gas Milage By # of Cylinders and Model",
         xlab = "Miles Per Gallon",
         color = "black",
         color = x$color)

# Dot chart

# Configure data

x <- mtcars[order(mtcars$mpg), ]

x$cyl <- factor(x$cyl)

x$color[x$cyl == 4] <- "violetred"

x$color[x$cyl == 6] <- "green3"

x$color[x$cyl == 8] <- "royalblue"

dotchart(x$mpg,

labels = row.names(x),

cex = 0.85,

pch = 16,

groups = x$cyl,

main = "Gas Milage By # of Cylinders and Model",

xlab = "Miles Per Gallon",

color = "black",

color = x$color)

Histogram

Click to enlarge

# Histogram
set.seed(9)
rnormMix <- ifelse(runif(5000) < 0.25, rnorm(5000, 3, 0.5),
rnorm(5000))

hist(rnormMix,
     breaks = 75,
     freq = FALSE,
     xaxp = c(-5, 5, 10),
     col = "steelblue",
     main = "Histogram With Line Density")
lines(density(rnormMix, n = 1000), col = "orange", lwd = 5 )

# Histogram

set.seed(9)

rnormMix <- ifelse(runif(5000) < 0.25, rnorm(5000, 3, 0.5),

rnorm(5000))

hist(rnormMix,

breaks = 75,

freq = FALSE,

xaxp = c(-5, 5, 10),

col = "steelblue",

main = "Histogram With Line Density")

lines(density(rnormMix, n = 1000), col = "orange", lwd = 5 )

The hist() function returns a useful statistical summary if the argument plot=FALSE is used. The summary defines the breakpoints for the bins and counts per bin. For example:

> hist(Abeline.WSpeed, plot = FALSE)
$breaks:
[1]  0  2  4  6  8 10 12 14 16
$counts:
[1] 1 2 7 4 7 1 1 2

> hist(Abeline.WSpeed, plot = FALSE)

$breaks:

[1] 0 2 4 6 8 10 12 14 16

$counts:

[1] 1 2 7 4 7 1 1 2

The following example shows how bin summary data can be used. Function pretty() takes a vector argument and divides its range into n equally spaced, ordered values, as shown in the histogram below:

Click to enlarge

# Horizontal Histogram
# Generate simulated wind speed data
set.seed(3)
Abilene <- rweibull(1500, shape = 2.00, scale = 6.0)
Lubock <- rweibull(1500, shape = 1.90, scale = 9.5)

# Summarize observation by WS bin
WSbins <- pretty(c(Abilene, Lubock), n = 10)
h1 <- hist(Abilene, breaks = WSbins, plot = FALSE)$counts
h2 <- hist(Lubock, breaks = WSbins, plot = FALSE)$counts

# Generate plot
barplot(matrix(c(h1, -h2), ncol = 14, byrow = TRUE),
        beside = T,
        space = c(-1,0),
        col = c("turquoise4", "plum2"),
        xlim = c(-400, 400),
        axes = FALSE,
        horiz = TRUE,
        legend.text = c("Abilene, TX", "Lubock, TX"),
        ylab = "December WindSpeed",
        xlab = "Observation Frequency: abs(x)")
        axis(1, at = seq(-400, 400, by = 100), line = -1.35)
        axis(2, at = WSbins)

# Horizontal Histogram

# Generate simulated wind speed data

set.seed(3)

Abilene <- rweibull(1500, shape = 2.00, scale = 6.0)

Lubock <- rweibull(1500, shape = 1.90, scale = 9.5)

# Summarize observation by WS bin

WSbins <- pretty(c(Abilene, Lubock), n = 10)

h1 <- hist(Abilene, breaks = WSbins, plot = FALSE)$counts

h2 <- hist(Lubock, breaks = WSbins, plot = FALSE)$counts

# Generate plot

barplot(matrix(c(h1, -h2), ncol = 14, byrow = TRUE),

beside = T,

space = c(-1,0),

col = c("turquoise4", "plum2"),

xlim = c(-400, 400),

axes = FALSE,

horiz = TRUE,

legend.text = c("Abilene, TX", "Lubock, TX"),

ylab = "December WindSpeed",

xlab = "Observation Frequency: abs(x)")

axis(1, at = seq(-400, 400, by = 100), line = -1.35)

axis(2, at = WSbins)

Using segments()

Click to enlarge

n = 17
theta = seq(0, 2 * pi, length = n + 1)[1:n]
x = sin(theta)
y = cos(theta)

plot.new()
plot.window(xlim = c(-1, 1),
            ylim = c(-1, 1),
            asp = 1)
for (i in 2:n){
   for (j in 1:(i-1)){
      segments(x[i], y[i], x[j], y[j], col = "magenta4")
   }
}
title(main = "A Rosette Plot")

n = 17

theta = seq(0, 2 * pi, length = n + 1)[1:n]

x = sin(theta)

y = cos(theta)

plot.new()

plot.window(xlim = c(-1, 1),

ylim = c(-1, 1),

asp = 1)

for (i in 2:n){

for (j in 1:(i-1)){

segments(x[i], y[i], x[j], y[j], col = "magenta4")

}

title(main = "A Rosette Plot")

Using polygon()

Click to enlarge

# Define data and color ramps
x <- c(-1, 1, 1, -1)
y <- c(-1, -1, 1, 1)
n <- 25
i1 <- 1:4
i2 <- c(2:4, 1)
ramp <- colorRamp(c("navy", "palegreen"))
c.ramp <- rgb(ramp(seq(0, 1, length = n)), max = 255)
# Plot loop
plot.new()
plot.window(xlim = c(-1, 1),
            ylim = c(-1, 1),
            asp = 1)
for(i in 1:n){
   polygon(x, y, col = c.ramp[i], border="grey50")
   x = 0.9 * x[i1] + .1 * x[i2]
   y = 0.9 * y[i1] + .1 * y[i2]
}
title(main = "A Spiral Square")

# Define data and color ramps

x <- c(-1, 1, 1, -1)

y <- c(-1, -1, 1, 1)

n <- 25

i1 <- 1:4

i2 <- c(2:4, 1)

ramp <- colorRamp(c("navy", "palegreen"))

c.ramp <- rgb(ramp(seq(0, 1, length = n)), max = 255)

# Plot loop

plot.new()

plot.window(xlim = c(-1, 1),

ylim = c(-1, 1),

asp = 1)

for(i in 1:n){

polygon(x, y, col = c.ramp[i], border="grey50")

x = 0.9 * x[i1] + .1 * x[i2]

y = 0.9 * y[i1] + .1 * y[i2]

}

title(main = "A Spiral Square")

Image Plot

Click to enlarge

# Image chart
x <- 10*(1:nrow(volcano))
y <- 10*(1:ncol(volcano))
image(x, y, volcano, col = terrain.colors(100), axes = FALSE)
contour(x, y, volcano, levels = seq(90, 200, by = 5), add = TRUE, col = "peru")
axis(1, at = seq(100, 800, by = 100))
axis(2, at = seq(100, 600, by = 100))
box()
title(main = "Topography of Maunga Whau Volcano")

# Image chart

x <- 10*(1:nrow(volcano))

y <- 10*(1:ncol(volcano))

image(x, y, volcano, col = terrain.colors(100), axes = FALSE)

contour(x, y, volcano, levels = seq(90, 200, by = 5), add = TRUE, col = "peru")

axis(1, at = seq(100, 800, by = 100))

axis(2, at = seq(100, 600, by = 100))

box()

title(main = "Topography of Maunga Whau Volcano")

Matplot

Click to enlarge

# Matplot
table(iris$Species) # a data.frame with 'Species' as factor
iS <- iris$Species == "setosa"
iV <- iris$Species == "versicolor"
matplot(c(1, 8), 
        c(0, 4.5),
        type = "n", 
        xlab = "Length",
        ylab = "Width",
        main = "Iris Petal and Sepal Dimensions By Species")

u <- par("usr")
rect(u[1], u[3], u[2], u[4], col = "grey97")

matpoints(iris[iS,c(1,3)], 
          iris[iS,c(2,4)],
          pch = 16,
          col = c("skyblue", "green"))
matpoints(iris[iV,c(1,3)], 
          iris[iV,c(2,4)],
          pch = 17,
          col = c("skyblue4","green4"))

abline(h = 0:4, v = 0:8, col = "black", lty = 2, lwd = 0.2)
legend(1, 4, c(" Setosa Petals", " Setosa Sepals", "Versicolor Petals", 
               "Versicolor Sepals"),
       pch = c(16, 17),
       col = c("skyblue","skyblue4", "green","green4"))

# Matplot

table(iris$Species) # a data.frame with 'Species' as factor

iS <- iris$Species == "setosa"

iV <- iris$Species == "versicolor"

matplot(c(1, 8),

c(0, 4.5),

type = "n",

xlab = "Length",

ylab = "Width",

main = "Iris Petal and Sepal Dimensions By Species")

u <- par("usr")

rect(u[1], u[3], u[2], u[4], col = "grey97")

matpoints(iris[iS,c(1,3)],

iris[iS,c(2,4)],

pch = 16,

col = c("skyblue", "green"))

matpoints(iris[iV,c(1,3)],

iris[iV,c(2,4)],

pch = 17,

col = c("skyblue4","green4"))

abline(h = 0:4, v = 0:8, col = "black", lty = 2, lwd = 0.2)

legend(1, 4, c(" Setosa Petals", " Setosa Sepals", "Versicolor Petals",

"Versicolor Sepals"),

pch = c(16, 17),

col = c("skyblue","skyblue4", "green","green4"))

Pairs Plot

Click to enlarge

# Pairs plot
pairs(iris[1:4],
      main = "Anderson's Iris Data",
      pch = 21,
      bg = c("red", "green2", "steelblue4")[unclass(iris$Species)])

# Pairs plot

pairs(iris[1:4],

main = "Anderson's Iris Data",

pch = 21,

bg = c("red", "green2", "steelblue4")[unclass(iris$Species)])

Perspective Plot

Click to enlarge

# Perspective plot
z <- 2 * volcano # Exaggerate the relief
x <- 10 * (1:nrow(z)) # 10 meter spacing (S to N)
y <- 10 * (1:ncol(z)) # 10 meter spacing (E to W)

persp(x, y, z,
      theta = 135,
      phi = 30,
      col = "green2",
      scale = FALSE,
      ltheta = -120,
      shade = 0.75,
      border = NA,
      box = FALSE)
title(main = "Topography of Maunga Whau Volcano")

# Perspective plot

z <- 2 * volcano # Exaggerate the relief

x <- 10 * (1:nrow(z)) # 10 meter spacing (S to N)

y <- 10 * (1:ncol(z)) # 10 meter spacing (E to W)

persp(x, y, z,

theta = 135,

phi = 30,

col = "green2",

scale = FALSE,

ltheta = -120,

shade = 0.75,

border = NA,

box = FALSE)

title(main = "Topography of Maunga Whau Volcano")

Pie Chart

Click to enlarge

# Pie chart
pie(rep(1, 24), col = rainbow(24), radius = .9)

1 2	# Pie chart pie(rep(1, 24), col = rainbow(24), radius = .9)

QQNorm Plot

Click to enlarge

# QQNorm plot
y <- rt(200, df = 5)
qqnorm(y,
       pch = 16,
       col = "steelblue4",
       bg = "ivory")
abline(h = -4:4, v = -4:4, col = "black", lty = 2, lwd = 0.2)
qqline(y,
       col = "green2",
       lwd=2.5)

# QQNorm plot

y <- rt(200, df = 5)

qqnorm(y,

pch = 16,

col = "steelblue4",

bg = "ivory")

abline(h = -4:4, v = -4:4, col = "black", lty = 2, lwd = 0.2)

qqline(y,

col = "green2",

lwd=2.5)

Back | Next

Simple Line Plot

Bar Plot of Vector

Bar Plot of Matrix: #1

Bar Plot of Matrix: #2

Box Plot

Contour Plot

Dot Chart

Histogram

Using segments()

Using polygon()

Image Plot

Matplot

Pairs Plot

Perspective Plot

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